We are developing computational methods for aligning neural representational
spaces across subjects at a fine spatial scale. Using these methods, we can
now use multivariate pattern (MVP) analyses to build a model on a group of
subjects and use that model to classify responses in a new subject. We are
using these methods to build a common high-dimensional model of the
representational space in ventral temporal (VT) cortex for complex visual
stimuli.

The distributed neural system for face perception, recognition of familiar
faces, and the role of face perception in social communication are longstanding
research themes in the lab. Current projects include investigations of the
automaticity of face recognition and activation of person knowledge – to what
extent can these processes be accomplished with minimal attention or without
conscious awareness.

The ventral object vision pathway in the human brain appears to have a
lateral-to-medial topography in ventral temporal cortex that reflects a
distinction between the representation of animate and inanimate stimuli. A
similar distinction is found in lateral temporal cortex (superior temporal
sulcus to middle temporal gyrus). MVP analysis of suggests that the
representations of animate entities in ventral temporal cortex embodies
semantic knowledge of animal species.